Multi-objective optimization of fed-batch bioreactor for lysine production

ABSTRACT

Lysine production via the fermentation process is one of the most economical production routes that involves simultaneous conflicting objectives. Two multi-objective differential evolution algorithms MODE-III and MODE-III-IMS are used to obtain the optimal control parameters of the lysine bioreactor. Two conflicting objectives, namely yield and productivity, are studied. Singular and constant feeding policies using both algorithms are studied, and respective Pareto fronts are reported. Tournament selection and penalty constraint handling methods are used, and their performances are compared for both algorithms. The higher bound of feeding rate (2.0 L/s) is found to be the best feeding rate compared to other feeding rates. The MODE-III-IMS algorithm converged to the Pareto front faster than the MODE-III algorithm. The COPRAS method is used to carry out the Pareto ranking, and the best optimal solution is reported. The decision tree method is used to predict and report the best optimal solution with acceptable accuracy.

KEYWORDS:

• Lysine
• Productivity
• Yield
• Constraints
• MODE
• Biochemical
• Fed-batch
• Bioreactor
• Machine learning
• Pareto ranking

Disclosure statement

No potential conflict of interest was reported by the authors.